Big data-driven service level analysis for a retail store

Using simulation technology, a procedure is proposed for a big data-driven service-level analysis for a real retail store. First, a data generator is designed to randomly select a sample of an expected number of customers or sampling data on a certain day from a large-scale dataset of sales predefined. Second, the clerk schedules are inputted into a data table created using Excel. Finally, simulation modeling mimics the service process of the retail store to examine and analyze the customer service level based on the selected data and the inputted clerk schedules. The proposed procedure for big data-driven service-level analysis shows the relations between the influencing service-level elements between the number of customers coming into stores, the frequency of customers, and the average customer service time. The procedure is generic and can easily be used to examine the service level in the remote past or to analyze and forecast the future.

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